Overview

Dataset statistics

Number of variables12
Number of observations2987
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory291.7 KiB
Average record size in memory100.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qty_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qty_invoicesHigh correlation
qty_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_items_purchased is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
products_variety is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_variety_basketHigh correlation
avg_recency_days is highly overall correlated with frequency_daysHigh correlation
frequency_days is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_variety_basket is highly overall correlated with products_variety and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 24.66292639)Skewed
total_return is highly skewed (γ1 = 22.05429318)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.1%) zerosZeros
total_return has 1500 (50.2%) zerosZeros

Reproduction

Analysis started2023-01-31 17:53:48.150959
Analysis finished2023-01-31 17:54:08.591321
Duration20.44 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2987
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15268.629
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-01-31T14:54:08.734735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12613.6
Q113791.5
median15220
Q316770.5
95-th percentile17964.7
Maximum18287
Range5940
Interquartile range (IQR)2979

Descriptive statistics

Standard deviation1721.4222
Coefficient of variation (CV)0.11274242
Kurtosis-1.2080253
Mean15268.629
Median Absolute Deviation (MAD)1491
Skewness0.030183419
Sum45607394
Variance2963294.3
MonotonicityNot monotonic
2023-01-31T14:54:08.895694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
13223 1
 
< 0.1%
12670 1
 
< 0.1%
17588 1
 
< 0.1%
14759 1
 
< 0.1%
16185 1
 
< 0.1%
13505 1
 
< 0.1%
13389 1
 
< 0.1%
17631 1
 
< 0.1%
17061 1
 
< 0.1%
Other values (2977) 2977
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12363 1
< 0.1%
12364 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2972
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2677.8825
Minimum5.9
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:09.056024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile227.507
Q1566.59
median1079.34
Q32296.855
95-th percentile7123.119
Maximum279138.02
Range279132.12
Interquartile range (IQR)1730.265

Descriptive statistics

Standard deviation10083.327
Coefficient of variation (CV)3.7654107
Kurtosis401.66922
Mean2677.8825
Median Absolute Deviation (MAD)667.74
Skewness17.717157
Sum7998835
Variance1.0167349 × 108
MonotonicityNot monotonic
2023-01-31T14:54:09.163080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731.9 2
 
0.1%
745.06 2
 
0.1%
1025.44 2
 
0.1%
178.96 2
 
0.1%
598.2 2
 
0.1%
533.33 2
 
0.1%
379.65 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
889.93 2
 
0.1%
Other values (2962) 2967
99.3%
ValueCountFrequency (%)
5.9 1
< 0.1%
6.2 1
< 0.1%
13.3 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
63 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
136275.72 1
< 0.1%
124564.53 1
< 0.1%
116729.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.818212
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:09.297745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median32
Q384
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)73

Descriptive statistics

Standard deviation78.0885
Coefficient of variation (CV)1.2047309
Kurtosis2.7156163
Mean64.818212
Median Absolute Deviation (MAD)26
Skewness1.7838576
Sum193612
Variance6097.8139
MonotonicityNot monotonic
2023-01-31T14:54:09.437142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.8%
2 84
 
2.8%
8 76
 
2.5%
10 67
 
2.2%
7 66
 
2.2%
9 66
 
2.2%
17 64
 
2.1%
15 55
 
1.8%
Other values (262) 2238
74.9%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 84
2.8%
3 85
2.8%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.5%
9 66
2.2%
10 67
2.2%
ValueCountFrequency (%)
373 2
0.1%
372 3
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 3
0.1%
364 1
 
< 0.1%
360 2
0.1%
359 1
 
< 0.1%
358 4
0.1%

qty_invoices
Real number (ℝ)

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6916639
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:09.569203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8227128
Coefficient of variation (CV)1.5501114
Kurtosis191.0355
Mean5.6916639
Median Absolute Deviation (MAD)2
Skewness10.765303
Sum17001
Variance77.840262
MonotonicityNot monotonic
2023-01-31T14:54:09.690971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 788
26.4%
3 498
16.7%
4 394
13.2%
5 236
 
7.9%
1 206
 
6.9%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 69
 
2.3%
10 54
 
1.8%
Other values (47) 332
11.1%
ValueCountFrequency (%)
1 206
 
6.9%
2 788
26.4%
3 498
16.7%
4 394
13.2%
5 236
 
7.9%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 69
 
2.3%
10 54
 
1.8%
ValueCountFrequency (%)
206 1
< 0.1%
198 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%

qty_items_purchased
Real number (ℝ)

Distinct1678
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1574.2133
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:09.821549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile101
Q1294.5
median628
Q31395.5
95-th percentile4395.2
Maximum196844
Range196842
Interquartile range (IQR)1101

Descriptive statistics

Standard deviation5687.5781
Coefficient of variation (CV)3.6129654
Kurtosis519.9407
Mean1574.2133
Median Absolute Deviation (MAD)413
Skewness18.793868
Sum4702175
Variance32348544
MonotonicityNot monotonic
2023-01-31T14:54:09.956719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
134 8
 
0.3%
272 8
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
288 8
 
0.3%
114 8
 
0.3%
246 8
 
0.3%
Other values (1668) 2902
97.2%
ValueCountFrequency (%)
2 3
0.1%
12 2
0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80179 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57872 1
< 0.1%
50255 1
< 0.1%

products_variety
Real number (ℝ)

Distinct339
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.019083
Minimum1
Maximum1785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:10.101940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q125
median52
Q3100
95-th percentile233
Maximum1785
Range1784
Interquartile range (IQR)75

Descriptive statistics

Standard deviation96.634915
Coefficient of variation (CV)1.2229314
Kurtosis82.657155
Mean79.019083
Median Absolute Deviation (MAD)33
Skewness6.3987239
Sum236030
Variance9338.3067
MonotonicityNot monotonic
2023-01-31T14:54:10.213647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 44
 
1.5%
18 42
 
1.4%
37 40
 
1.3%
28 40
 
1.3%
25 37
 
1.2%
15 37
 
1.2%
11 37
 
1.2%
26 37
 
1.2%
23 36
 
1.2%
30 36
 
1.2%
Other values (329) 2601
87.1%
ValueCountFrequency (%)
1 26
0.9%
2 16
0.5%
3 20
0.7%
4 20
0.7%
5 35
1.2%
6 22
0.7%
7 26
0.9%
8 30
1.0%
9 32
1.1%
10 27
0.9%
ValueCountFrequency (%)
1785 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
884 1
< 0.1%
816 1
< 0.1%
717 1
< 0.1%
713 1
< 0.1%
699 1
< 0.1%
636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2985
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.191821
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:10.335112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9309583
Q113.111564
median17.931321
Q324.84247
95-th percentile90.11325
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.730905

Descriptive statistics

Standard deviation120.28843
Coefficient of variation (CV)3.6240381
Kurtosis788.44422
Mean33.191821
Median Absolute Deviation (MAD)5.9524292
Skewness24.662926
Sum99143.97
Variance14469.305
MonotonicityNot monotonic
2023-01-31T14:54:10.443592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.162 2
 
0.1%
14.47833333 2
 
0.1%
32.64 1
 
< 0.1%
31.5408046 1
 
< 0.1%
53.92173913 1
 
< 0.1%
15.94088235 1
 
< 0.1%
17.07774194 1
 
< 0.1%
17.22099526 1
 
< 0.1%
15.64627451 1
 
< 0.1%
13.54692308 1
 
< 0.1%
Other values (2975) 2975
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
931.5 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1254
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.852708
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:10.589601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.4329798
Q125.267857
median47.666667
Q384.875
95-th percentile200.7
Maximum366
Range365
Interquartile range (IQR)59.607143

Descriptive statistics

Standard deviation63.423688
Coefficient of variation (CV)0.94870784
Kurtosis4.9626506
Mean66.852708
Median Absolute Deviation (MAD)26.121212
Skewness2.0767769
Sum199689.04
Variance4022.5642
MonotonicityNot monotonic
2023-01-31T14:54:10.705913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 24
 
0.8%
4 23
 
0.8%
70 22
 
0.7%
7 20
 
0.7%
1 19
 
0.6%
46 18
 
0.6%
21 18
 
0.6%
35 18
 
0.6%
11 18
 
0.6%
49 18
 
0.6%
Other values (1244) 2789
93.4%
ValueCountFrequency (%)
1 19
0.6%
1.5 1
 
< 0.1%
2 14
0.5%
2.5 1
 
< 0.1%
2.565517241 1
 
< 0.1%
3 15
0.5%
3.271929825 1
 
< 0.1%
3.321428571 1
 
< 0.1%
3.5 2
 
0.1%
4 23
0.8%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency_days
Real number (ℝ)

Distinct1223
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.64533
Minimum0.058823529
Maximum183.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:10.827601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.058823529
5-th percentile1
Q119.972222
median38.444444
Q361
95-th percentile112.33333
Maximum183.5
Range183.44118
Interquartile range (IQR)41.027778

Descriptive statistics

Standard deviation34.161602
Coefficient of variation (CV)0.7651775
Kurtosis1.9050532
Mean44.64533
Median Absolute Deviation (MAD)20.444444
Skewness1.2373278
Sum133355.6
Variance1167.015
MonotonicityNot monotonic
2023-01-31T14:54:10.970453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 214
 
7.2%
16 18
 
0.6%
36 17
 
0.6%
42 16
 
0.5%
29 15
 
0.5%
11 15
 
0.5%
12 15
 
0.5%
34 14
 
0.5%
47 13
 
0.4%
39 13
 
0.4%
Other values (1213) 2637
88.3%
ValueCountFrequency (%)
0.05882352941 1
 
< 0.1%
0.3333333333 1
 
< 0.1%
0.5 7
 
0.2%
0.875 1
 
< 0.1%
1 214
7.2%
1.333333333 1
 
< 0.1%
1.5 3
 
0.1%
1.815533981 1
 
< 0.1%
1.883838384 1
 
< 0.1%
2 3
 
0.1%
ValueCountFrequency (%)
183.5 1
 
< 0.1%
183 1
 
< 0.1%
182.5 1
 
< 0.1%
182 1
 
< 0.1%
179 2
0.1%
178.5 1
 
< 0.1%
178 2
0.1%
176.5 1
 
< 0.1%
176 2
0.1%
175.5 3
0.1%

total_return
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.572481
Minimum0
Maximum9014
Zeros1500
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:11.102129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile98
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation281.93743
Coefficient of variation (CV)8.1549667
Kurtosis600.34493
Mean34.572481
Median Absolute Deviation (MAD)0
Skewness22.054293
Sum103268
Variance79488.714
MonotonicityNot monotonic
2023-01-31T14:54:11.213258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1500
50.2%
1 164
 
5.5%
2 149
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.0%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (203) 704
23.6%
ValueCountFrequency (%)
0 1500
50.2%
1 164
 
5.5%
2 149
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.0%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3331 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct1985
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.74411
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:11.325962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.066667
Q1103.275
median172.25
Q3282
95-th percentile599.64
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.725

Descriptive statistics

Standard deviation284.14695
Coefficient of variation (CV)1.2002281
Kurtosis101.78121
Mean236.74411
Median Absolute Deviation (MAD)83.25
Skewness7.6532698
Sum707154.66
Variance80739.487
MonotonicityNot monotonic
2023-01-31T14:54:11.439780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 11
 
0.4%
73 9
 
0.3%
86 9
 
0.3%
136 9
 
0.3%
82 9
 
0.3%
60 9
 
0.3%
140 8
 
0.3%
163 8
 
0.3%
75 8
 
0.3%
Other values (1975) 2896
97.0%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

avg_variety_basket
Real number (ℝ)

Distinct911
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.542001
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-31T14:54:11.613097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.7298387
median13.625
Q322.2
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.470161

Descriptive statistics

Standard deviation15.48703
Coefficient of variation (CV)0.88285424
Kurtosis28.897321
Mean17.542001
Median Absolute Deviation (MAD)6.625
Skewness3.4059968
Sum52397.956
Variance239.84808
MonotonicityNot monotonic
2023-01-31T14:54:11.758232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 43
 
1.4%
9 42
 
1.4%
16 40
 
1.3%
8 39
 
1.3%
14 39
 
1.3%
17 38
 
1.3%
5 38
 
1.3%
7 38
 
1.3%
11 37
 
1.2%
15 35
 
1.2%
Other values (901) 2598
87.0%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-01-31T14:54:06.655984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:48.461019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.944826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:51.799215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.364450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.931810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.515329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:58.372215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.002558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.500462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.145303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.604143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:06.781051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:48.587927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.042690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:51.959194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.483706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.064760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.628895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:58.522825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.154954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.676762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.299066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.761823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:06.906011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:48.687712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.141859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.109317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.592678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.195189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.735606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:58.673555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.284737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.784315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.442032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.948992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.018753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:48.833131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.267640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.238862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.747447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.360609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.891821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:58.844151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.397184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.945798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.544808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.166086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.112638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:48.928490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.400843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.344596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.901592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.503751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.034823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:58.935803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.496879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.040096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.640612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.260165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.221118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.083350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.517510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.457176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.054702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.659845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.164155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.099648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.612795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.226691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.776801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.412233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.348219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.229200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.685600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.625874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.236942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.779857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.294907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.260722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.765809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.349822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.883614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.539728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.456302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.322961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.818254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.764484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.380552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:55.886953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.411978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.369809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:00.889760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.485848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.995687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.674026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.578708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.446823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:50.934113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:52.887131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.494958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.012430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.528210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.517171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.042474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.657499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.112240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.790775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.711430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.550658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:51.098216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.009334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.602542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.148108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.654787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.651993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.158650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.783338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.227916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:05.974068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.830540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.648795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:51.229269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.120965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.720634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.280596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:57.783067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.782755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.260739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:02.910114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.381810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:06.100815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:07.957672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:49.794953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:51.396691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:53.226727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:54.839047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:56.408885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:58.249213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:53:59.903428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:01.365932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:03.016396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:04.508552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-31T14:54:06.535795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-31T14:54:11.888245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysqty_invoicesqty_items_purchasedproducts_varietyavg_ticketavg_recency_daysfrequency_daystotal_returnavg_basket_sizeavg_variety_basket
customer_id1.000-0.0760.0010.027-0.0700.006-0.1300.0230.002-0.063-0.125-0.020
gross_revenue-0.0761.000-0.4170.7720.9250.6670.246-0.250-0.0800.3750.5720.105
recency_days0.001-0.4171.000-0.507-0.409-0.3840.0480.107-0.027-0.124-0.0950.016
qty_invoices0.0270.772-0.5071.0000.7180.5850.061-0.251-0.0600.3010.099-0.183
qty_items_purchased-0.0700.925-0.4090.7181.0000.6640.169-0.231-0.0710.3470.7270.146
products_variety0.0060.667-0.3840.5850.6641.000-0.452-0.1090.0120.2130.3860.636
avg_ticket-0.1300.2460.0480.0610.169-0.4521.000-0.128-0.0880.1890.189-0.615
avg_recency_days0.023-0.2500.107-0.251-0.231-0.109-0.1281.0000.871-0.394-0.0870.129
frequency_days0.002-0.080-0.027-0.060-0.0710.012-0.0880.8711.000-0.224-0.0290.116
total_return-0.0630.375-0.1240.3010.3470.2130.189-0.394-0.2241.0000.208-0.054
avg_basket_size-0.1250.572-0.0950.0990.7270.3860.189-0.087-0.0290.2081.0000.404
avg_variety_basket-0.0200.1050.016-0.1830.1460.636-0.6150.1290.116-0.0540.4041.000

Missing values

2023-01-31T14:54:08.194675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T14:54:08.471951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqty_invoicesqty_items_purchasedproducts_varietyavg_ticketavg_recency_daysfrequency_daystotal_returnavg_basket_sizeavg_variety_basket
0178505391.21372.034.01733.021.018.15222235.5000000.05882440.050.9705880.617647
1130473232.5956.09.01390.0105.018.90403526.30769235.33333335.0154.44444411.666667
2125836705.382.015.05028.0114.028.90250021.82352924.80000050.0335.2000007.600000
313748948.2595.05.0439.024.033.86607192.66666755.8000000.087.8000004.800000
415100876.00333.03.080.01.0292.0000008.60000013.66666722.026.6666670.333333
5152914623.3025.014.02102.061.045.32647121.75000024.92857129.0150.1428574.357143
6146885630.877.021.03621.0148.017.21978618.30000017.476190399.0172.4285717.047619
7178095411.9116.012.02057.046.088.71983632.45454529.83333341.0171.4166673.833333
81531160767.900.091.038194.0567.025.5434644.1444444.109890474.0419.7142866.230769
9160982005.6387.07.0613.034.029.93477647.66666741.0000000.087.5714294.857143
customer_idgross_revenuerecency_daysqty_invoicesqty_items_purchasedproducts_varietyavg_ticketavg_recency_daysfrequency_daystotal_returnavg_basket_sizeavg_variety_basket
5611177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
562117232421.522.02.0203.030.011.70888912.06.5000000.0101.50000015.000000
562217468137.0010.02.0116.05.027.4000004.02.5000000.058.0000002.500000
563313596697.045.02.0406.0133.04.1990367.04.0000000.0203.00000066.500000
5639148931237.859.02.0799.072.016.9568492.01.5000000.0399.50000036.000000
564312479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.000000
566414126706.137.03.0508.014.047.0753333.01.33333350.0169.3333334.666667
5670135211092.391.03.0733.0312.02.5112414.53.3333330.0244.333333104.000000
568015060301.848.04.0262.080.02.5153331.00.5000000.065.50000020.000000
569912558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.000000